Performance Evaluation of Semantic Kriging: A Euclidean Vector Analysis Approach


Prediction of spatial attributes in geospatial data repositories is indispensable in the sphere of remote sensing and geographic data system. The semantic kriging (SemK) approach semantically captures the domain knowledge of the terrain in terms of native spatial options for spatial attribute prediction. It produces higher results than normal kriging and different prediction strategies. This letter focuses on the theoretical and empirical analyses of the SemK. A Euclidean vector analysis approach is adopted to theoretically prove the efficacy of SemK in capturing semantic data.

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